# Rethinking Software Engineering in the Foundation Model Era: From Task-Driven AI Copilots to Goal-Driven AI Pair Programmers

arXiv: https://arxiv.org/abs/2404.10225 · HTML: https://mlsanity.com/paper/2404.10225/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 49 references that are in the map.
- First posted on arXiv 16 Apr 2024.

## Builds on (most cited first, 6 of 6)

- [Program Synthesis with Large Language Models](https://mlsanity.com/paper/2108.07732/) (2021)
- [Making Pre-trained Language Models Better Few-shot Learners](https://mlsanity.com/paper/2012.15723/) (2020)
- [DSPy: Compiling Declarative Language Model Calls into Self-Improving Pipelines](https://mlsanity.com/paper/2310.03714/) (2023)
- [The Impact of AI on Developer Productivity: Evidence from GitHub Copilot](https://mlsanity.com/paper/2302.06590/) (2023)
- [Bugs in Large Language Models Generated Code: An Empirical Study](https://mlsanity.com/paper/2403.08937/) (2024)
- [How should my chatbot interact? A survey on human-chatbot interaction design](https://mlsanity.com/paper/1904.02743/) (2019)

## Built on by (most cited first, 12 of 12)

- [HumanEvalComm: Benchmarking the Communication Competence of Code Generation for LLMs and LLM Agent](https://mlsanity.com/paper/2406.00215/) (2024)
- [Beyond the Comfort Zone: Emerging Solutions to Overcome Challenges in Integrating LLMs into Software Products](https://mlsanity.com/paper/2410.12071/) (2024)
- [Can Code Language Models Learn Clarification-Seeking Behaviors?](https://mlsanity.com/paper/2504.16331/) (2025)
- [A Taxonomy of Architecture Options for Foundation Model-based Agents: Analysis and Decision Model](https://mlsanity.com/paper/2408.02920/) (2024)
- [Act As a Real Researcher: A Suite of Benchmarks Evaluating Frontier LLMs and Agentic Harnesses in Research Lifecycle](https://mlsanity.com/paper/2606.07462/) (2026)
- [CodePurify: Defend Backdoor Attacks on Neural Code Models via Entropy-based Purification](https://mlsanity.com/paper/2410.20136/) (2024)
- [LicenseGPT: A Fine-tuned Foundation Model for Publicly Available Dataset License Compliance](https://mlsanity.com/paper/2501.00106/) (2024)
- [SEALGuard: Safeguarding the Multilingual Conversations in Southeast Asian Languages for LLM Software Systems](https://mlsanity.com/paper/2507.08898/) (2025)
- [AdaptiveGuard: Towards Adaptive Runtime Safety for LLM-Powered Software](https://mlsanity.com/paper/2509.16861/) (2025)
- [Let's Make Every Pull Request Meaningful: An Empirical Analysis of Developer and Agentic Pull Requests](https://mlsanity.com/paper/2601.18749/) (2026)
- [DecipherGuard: Understanding and Deciphering Jailbreak Prompts for a Safer Deployment of Intelligent Software Systems](https://mlsanity.com/paper/2509.16870/) (2025)
- [Search-Based Software Engineering and AI Foundation Models: Current Landscape and Future Roadmap](https://mlsanity.com/paper/2505.19625/) (2025)

## Abstract (arXiv)

The advent of Foundation Models (FMs) and AI-powered copilots has transformed the landscape of software development, offering unprecedented code completion capabilities and enhancing developer productivity. However, the current task-driven nature of these copilots falls short in addressing the broader goals and complexities inherent in software engineering (SE). In this paper, we propose a paradigm shift towards goal-driven AI-powered pair programmers that collaborate with human developers in a more holistic and context-aware manner. We envision AI pair programmers that are goal-driven, human partners, SE-aware, and self-learning. These AI partners engage in iterative, conversation-driven development processes, aligning closely with human goals and facilitating informed decision-making. We discuss the desired attributes of such AI pair programmers and outline key challenges that must be addressed to realize this vision. Ultimately, our work represents a shift from AI-augmented SE to AI-transformed SE by replacing code completion with a collaborative partnership between humans and AI that enhances both productivity and software quality.